Adaptive Distributed Outlier Detection for WSNs
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 341
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2014.2338611
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 902
- Volume
- 45
- Issue
- 5
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- URL
-
https://ieeexplore.ieee.org/document/6863636
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The culmination of 9 years' CS/Electronic Engineering collaboration on Wireles Sensor Networks at DICGIM, University of Palermo, with over 20 conference Journal papers (e.g. https://doi.org/10.1016/j.pmcj.2011.02.006; https://doi.org/10.1002/cpe.3311), the research led to Keele collaborative research on energy efficiency and smart homes with DICGIM (e.g. doi.org/10.1007/s12652-019-01375-2), and Missouri University of Science and Technology (e.g. https://doi.org/10.1109/TITS.2018.2829086) The algorithm and results presented in the paper have been used elsewhere (e.g. as a baseline for outlier detection, by Bharti et al. doi.org/10.1007/s12652-019-01194-5). Ortolani and De Paola jointly designed the Bayesian outlier detection algorithm.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -